Senior Scientist II, Associate Principal - Computational Biology

Summary

Join Tempus as a Senior Scientist II - Associate Principal Scientist to design and implement clinicogenomic studies, analyze high-dimensional genomic data, and apply best-in-class algorithms or develop new ones to address important biological and clinical questions.

Requirements

• PhD degree in a quantitative discipline (e.g. Computational Biology, Cancer Biology, Computational Immunology, Biostatistics/Statistical Genetics, Bioinformatics, or similar)
• Minimum 2+ years leveraging genomic and multimodal with machine learning approaches to address questions in complex diseases, especially cancer
• Proficient in R, Python, and SQL
• Strong understanding of Cancer, Genomics, and/or Immunology
• Extensive prior experience analyzing genomic data, including whole exome sequencing, whole transcriptome sequencing, and/or TCR/BCR immune repertoire sequencing, single-cell RNA sequencing, spatial transcriptomics
• Demonstrated ability to lead and manage complex projects and collaborate with cross-functional teams
• Strong strategic thinking and problem-solving skills
• Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences

Responsibilities

• Design, develop, and execute multi-modal computational biology and/or immunology research projects of high complexity
• Support exploratory research, development and validation studies for molecular subtype characterization, target discovery, and predictive algorithms
• Develop and implement scientific strategies and experimental work plans, including identifying new methodologies and approaches for large clinicogenomic databases and real world data
• Work closely with other cross-functional teams across the R&D and broader Tempus organization (product engineering, operations, clinical genomics labs, medical, science, data science, etc) to communicate research and integrate work plans and approaches
• Work with Product and Engineering teams to streamline the workflow of computational analyses

Preferred Qualifications

• PhD with 2+ to 6+ years of work experience
• Education and experience must combine: Quantitative and computational skills for biological data analysis (e.g. Computational Biology, Biostatistics/Statistical Genetics, Bioinformatics, Biomedical Informatics, or Data Science for Health)
• Biological or medical knowledge (e.g. Oncology, Genetics/Genomics, Molecular Biology, or Immunology)
• Multidisciplinary project team leadership experience
• Ability to deliver actionable insights from NGS, clinical, and/or real-world data sets
• Strong understanding of statistical techniques and artificial intelligence in computational biology with experience in integrative modeling of multi-modal clinical and omics data
• Previous experience working with large transcriptome and NGS data sets, including TCR-seq
• Experience with in R or Python packages for computational biology: Pandas, NumPy, SciPy, Scikit-learn, Jupyter Notebooks, RStudio, tidyverse, ggplot, Git, matplotlib, seaborn, Plot.ly, Docker, AWS
• Experience with R package development